eprintid: 10258
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/02/58
datestamp: 2023-11-09 16:36:53
lastmod: 2023-11-09 16:36:53
status_changed: 2023-11-09 16:30:57
type: article
metadata_visibility: show
creators_name: Ullah, S.
creators_name: Daud, H.
creators_name: Dass, S.C.
creators_name: Hadi, F.-T.
creators_name: Khalil, A.
title: An eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in khyber-pakhtunkhwa, Pakistan
ispublished: pub
keywords: algorithm; Article; case study; controlled study; disease classification; disease surveillance; health survey; human; information processing; measles; Pakistan; population research; risk factor; algorithm; measles; season; spatiotemporal analysis, Algorithms; Humans; Measles; Pakistan; Seasons; Space-Time Clustering
note: cited By 4
abstract: Identifying the abnormally high-risk regions in a spatiotemporal space that contains an unexpected disease count is helpful to conduct surveillance and implement control strategies. The EigenSpot algorithm has been recently proposed for detecting space-time disease clusters of arbitrary shapes with no restriction on the distribution and quality of the data, and has shown some promising advantages over the state-of-the-art methods. However, the main problem with the EigenSpot method is that it cannot be adapted to detect more than one spatiotemporal hotspot. This is an important limitation, since, in reality, we may have multiple hotspots, sometimes at the same level of importance. We propose an extension of the EigenSpot algorithm, called Multi-EigenSpot that is able to handle multiple hotspots by iteratively removing previously detected hotspots and re-running the algorithm until no more hotspots are found. In addition, a visualization tool (heatmap) has been linked to the proposed algorithm to visualize multiple clusters with different colors. We evaluated the proposed method using the monthly data on measles cases in Khyber-Pakhtunkhwa, Pakistan (Jan 2016- Dec 2016), and the efficiency was compared with the state-of-the-art methods: EigenSpot and Space-time scan statistic (SaTScan). The results showed the effectiveness of the proposed method for detecting multiple clusters in a spatiotemporal space. © 2018 Ullah et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
date: 2018
publisher: Public Library of Science
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048760498&doi=10.1371%2fjournal.pone.0199176&partnerID=40&md5=5cb204931ec13aab8a6ea5989bf9795e
id_number: 10.1371/journal.pone.0199176
full_text_status: none
publication: PLoS ONE
volume: 13
number: 6
refereed: TRUE
issn: 19326203
citation:   Ullah, S. and Daud, H. and Dass, S.C. and Hadi, F.-T. and Khalil, A.  (2018) An eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in khyber-pakhtunkhwa, Pakistan.  PLoS ONE, 13 (6).   ISSN 19326203